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This project aim to understad if a deep learning model is calibrated (average accuracy match average confident) using Reliability Diagram and perform a re-calibration by the training with Focal Loss.
A serverless function is utilized to evaluate the divergence of a particular output from the established log-likelihood set by a language model. This function is designed to compute the log-likelihood per message. Subsequently, p-values are generated and used as a prediction interval to categorize, appropriately append, and sort LLM output
This is a small demo of Conformal Prediction given to Prof. Glenn Shafer's class in Feb-2018. Compares Conformal Prediction to plain Linear Regression using the Boston dataset from R
Conformal Bayesian Computation (CBC). This paper summarizes the theoretical foundings of the CBC, as well as it applies to 2 use-cases: classification and regression.